Materials Science

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Computational Materials Science: Modeling Materials

Use the Amsterdam Modeling Suite for atomistic materials modeling across crystals, surfaces, interfaces, polymers, porous materials, batteries, organic electronics, semiconductors, catalysts, and reactive materials.

AMS combines periodic DFT with BAND and Quantum ESPRESSO, DFTB and MOPAC, ReaxFF, machine learning potentials, force fields, active learning, and PLAMS scripting in one graphical and scriptable environment.

Selected applications

Key methods

BAND and Quantum ESPRESSO for periodic DFT, band structures, work functions, phonons, spectra, and surfaces
MACE, UMA, M3GNet, and external ASE models for machine learning potential workflows
ReaxFF for reactive molecular dynamics in polymers, batteries, fuel cells, corrosion, and surface chemistry
GFN-FF, APPLE&P, UFF, AMBER, and GAFF for faster atomistic modeling
Simple Active Learning and ParAMS for fitting ReaxFF, DFTB, and ML potentials
PES exploration, molecular dynamics, and Monte Carlo through the AMS Driver

Example application: sputtering

Perovskite band gaps, DOS and COOP

“What I really like about the Amsterdam Modeling Suite is that the programs were clearly written by chemists for dealing with real chemical problems. A great suite of programs!”

Roald Hoffmann, Nobel Laureate

Why not try out these applications for yourself?

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Electronic Structure

ADF

Periodic DFT

DFTB & MOPAC

Interatomic Potentials

ReaxFF

Machine Learning Potentials

Force Fields

kMC and Microkinetics

Bumblebee: OLED stacks

Fluid Thermodynamics

COSMO-RS

Workflows and Utilities

OLED workflows

ChemTraYzer2

Conformers

Reactions Discovery

AMS Driver

Properties

PES Exploration

Molecular Dynamics

Monte Carlo

Interfaces

ParAMS

PLAMS

GUI

VASP

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Windows

Mac

Linux

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Tutorials

Installation Manual

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Pricing and licensing

Computational Materials Science: Modeling Materials

Selected applications

Key methods

Example application: sputtering

Perovskite band gaps, DOS and COOP